The octopus potential
The most valuable thing SaaS companies can do with AI is enabled by the technology, but goes far beyond that: they can help their customers distribute intelligence and authority throughout their organization. Or to put it another way, they can enable them to become an octopus organization – able to act and react faster because decisions are made closer to the grassroots.
This is demonstrated by the “Da Vinci” platform from the SaaS provider Movable Ink. The system enables the mass sending of highly personalized emails by combining vision models, generative AI, insight engines and prediction algorithms. The platform transfers complex personalization decisions to the marketing employees at the base. They had to obtain prior approval from management for changes to campaigns. The system now determines which stories are delivered to the customer, when and how often – and also which creative elements are used.
This decentralizes decision-making powers on a large scale: In a team with AI, each individual marketing employee becomes significantly more powerful and can make thousands of micro-decisions – which would have been unthinkable in traditional hierarchies. The example makes it clear: Successful SaaS products help customers become “octopus organizations” – distributed, adaptable and intelligent at every point.
The SaaS reality
Unfortunately, the reality is different. Most SaaS companies can’t help their customers become an octopus because they aren’t one themselves. An octopus has a “neural necklace,” a ring of nerve bundles that connects its arms and allows instant information exchange between them—without having to turn on the central brain. However, these connections are often broken in SaaS companies.
Just look at the customer success and product teams: the former learn where products fail, where workflows cause friction, and where latent needs remain unmet. The second has telemetry and performance data. When this information flows freely between teams, an extraordinary level of perception emerges. Typically, however, they have separate reporting channels: they exchange cleaned-up summaries – and critical signals are lost.
Ramamurthy addressed this problem at Rithum by integrating AI first into internal dashboards and then into external functions. The result was a common understanding across all functions. When customer success, product, and engineering teams access the same AI-generated customer behavior insights, they can develop a common language and align priorities.
